2022
NAACL
NAACL 2022
What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review
Abstract
AbstractBoth scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has been mostly approached as an automated recommendation problem rather than as a matter where different stakeholders (area chairs, reviewers, authors) have accumulated experience worth taking into account. We present the results of the first survey of the NLP community, identifying common issues and perspectives on what factors should be considered by paper-reviewer matching systems. This study contributes actionable recommendations for improving future NLP conferences, and desiderata for interpretable peer review assignments.
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The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Machine Learning
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Keyword Pioneer
— paper matching
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Application Areas > Fairness
Data Science & Analytics > Applications > Recommender Systems
Data Science & Analytics > Applications > Information Retrieval
Artificial Intelligence > Core AI > Knowledge Representation
Machine Learning > Application Areas > Transfer Learning
Machine Learning > Application Areas > Recommender Systems